Who should use the Evaluating model performance workflow?
Teams or solo builders working on development tasks who want a repeatable process instead of one-off tool experiments.
AI Workflow · Development
Practical execution plan for evaluating model performance with clear steps, mapped tools, and delivery-focused outcomes.
Deliverable outcome
A clear go/no-go decision with supporting evidence, enabling the team to proceed to deployment or iterate on the model.
30-90 minutes
Includes setup plus initial result generation
Free to start
You can swap tools by pricing and policy requirements
A clear go/no-go decision with supporting evidence, enabling the team to proceed to deployment or iterate on the model.
Use each step output as the input for the next stage
Step map
Instead of relying on a single generic AI model, this pipeline connects specialized tools to maximize quality. First, you'll use Roboflow to a documented evaluation plan with chosen metrics, a clean test dataset, and predefined pass/fail thresholds. Then, you pass the output to Modal AI to a complete set of predictions and metadata for every test sample, ready for metric calculation. Then, you pass the output to scikit-learn to a metrics report with tables and plots that clearly show overall and per-class performance, including any threshold tuning opportunities. Then, you pass the output to Captum to a categorized error report with visual explanations, providing actionable insights for model improvement. Then, you pass the output to Weights & Biases to a statistically validated comparison showing whether the new model is significantly better, worse, or equivalent to the baseline. Finally, ONNX Runtime is used to a clear go/no-go decision with supporting evidence, enabling the team to proceed to deployment or iterate on the model.
Define evaluation criteria and prepare test dataset
A documented evaluation plan with chosen metrics, a clean test dataset, and predefined pass/fail thresholds.
Run inference on test dataset
A complete set of predictions and metadata for every test sample, ready for metric calculation.
Calculate and visualize performance metrics
A metrics report with tables and plots that clearly show overall and per-class performance, including any threshold tuning opportunities.
Conduct error analysis and root cause investigation
A categorized error report with visual explanations, providing actionable insights for model improvement.
Benchmark against baseline and prior versions
A statistically validated comparison showing whether the new model is significantly better, worse, or equivalent to the baseline.
Assess deployment readiness and make go/no-go decision
A clear go/no-go decision with supporting evidence, enabling the team to proceed to deployment or iterate on the model.
Identify the key performance metrics relevant to your model's task (e.g., accuracy, precision, recall, F1, mAP, latency) and curate a representative, labeled test dataset that the model has never seen during training. Ensure the dataset covers edge cases and real-world distribution.
Why Roboflow: Roboflow provides data annotation and dataset management for computer vision, which aligns with preparing a test dataset, and can be paired with an experiment tracker.
Load the trained model and run inference on the entire test dataset, recording predictions and associated metadata (e.g., confidence scores, timestamps). Use batch processing to ensure efficiency and reproducibility.
Why Modal AI: Modal AI offers scalable inference deployment and batch processing, suitable for running inference on a test dataset.
Compute the selected metrics by comparing predictions to ground truth, then generate visualizations (e.g., confusion matrix, precision-recall curve, ROC curve) to understand model behavior across different thresholds and classes.
Why scikit-learn: scikit-learn provides classification, regression, and clustering metrics directly, fitting the need for calculating performance metrics.
Manually inspect a sample of misclassified or low-confidence predictions to categorize error types (e.g., label noise, occlusion, domain shift). Use tools like activation maps or SHAP to understand model reasoning for critical failures.
Why Captum: Captum is an interpretability library for PyTorch models, directly supporting feature attribution and error analysis.
Compare current model performance against a baseline (e.g., a simple heuristic or previous model version) using the same test set. Compute statistical significance (e.g., paired bootstrap) to ensure improvements are not due to chance.
Why Weights & Biases: Weights & Biases is an experiment tracker that supports comparing metrics across runs, ideal for benchmarking against baselines.
Review all metrics, error analysis, and baseline comparison against the predefined acceptance thresholds. Consider non-functional requirements like inference latency, memory usage, and model size. Document the final decision and any conditions (e.g., 'deploy with monitoring' or 'retrain with more data').
Why ONNX Runtime: ONNX Runtime provides model inference acceleration and quantization, which are key for profiling deployment readiness.
§ Before you start
Teams or solo builders working on development tasks who want a repeatable process instead of one-off tool experiments.
No. Start with the top pick for each step, then replace tools only if they do not fit your pricing, compliance, or output needs.
Open the mapped task page and compare top options side by side. Prioritize output quality, integration fit, and predictable cost before scaling.
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